tensorflow/probability
Probability
TensorFlow Probability is a library for probabilistic reasoning and statistical analysis integrated with the TensorFlow ecosystem. It serves as a Bayesian deep learning framework, a probabilistic programming interface, and a variational inference engine, providing a toolset for Markov chain Monte Carlo sampling and tensor-based probabilistic modeling.
The project enables the construction of neural networks with probabilistic weights and the implementation of Bayesian neural networks to quantify prediction uncertainty. It provides specialized capabilities for hierarchical probabilistic modeling to share statistical strength across data groups and utilizes bijectors for random variable transformations to create complex distributions.
The library covers a broad surface of probabilistic inference, including sampling-based integral approximation, stochastic parameter optimization, and joint distribution modeling. It includes utilities for statistical data analysis, such as Bayesian logistic regression and vector-quantized variational autoencoders, utilizing batch-aware distribution semantics and automatic differentiation.
Features
- Probabilistic Machine Learning - Integrates probability distributions into neural networks to model uncertainty and perform statistical inference.
- Automatic Differentiation - Provides automatic differentiation mechanisms for calculating gradients in probabilistic models using the chain rule.
- Bayesian Neural Networks - Implements neural networks where weights and biases are treated as probability distributions to quantify prediction uncertainty.
- Markov Chain Monte Carlo Sampling - Provides a collection of MCMC sampling algorithms for approximating integrals over complex distributions.
- Probabilistic Layers - Provides neural network layers that incorporate uncertainty over the functions they represent instead of fixed weight values.
- Variational Inference Implementations - Provides implementations for approximating complex posterior distributions by minimizing divergence metrics.
- Variational Posterior Approximations - Approximates complex integrals by minimizing divergence between simple variational distributions and true posteriors.
- Probabilistic Reasoning Libraries - Provides a library for probabilistic reasoning and statistical analysis integrated with the TensorFlow ecosystem.
- Variational Inference - Implements variational inference to approximate complex probability distributions by optimizing the evidence lower bound.
- Bayesian Probabilistic Programming Frameworks - Serves as a comprehensive framework for building probabilistic models and performing Bayesian inference.
- Hierarchical Models - Constructs hierarchical probabilistic models to share statistical strength across interdependent variables and data groups.
- Monte Carlo Sampling - Provides Monte Carlo sampling techniques to approximate complex integrals and compute expectations.
- Probability Distributions - Provides a comprehensive collection of probability distributions with batch and broadcasting semantics.
- Tensor-Based Probabilistic Modeling - Performs statistical computations by representing probability distributions and operations as multi-dimensional arrays for hardware acceleration.
- Tensor-Based Probabilistic Reasoning - Implements mathematical tools to model uncertainty and perform statistical inference using tensor-based computations.
- Vector-Quantized VAEs - Implements vector-quantized VAEs that map continuous latent codes to discrete codebooks for representation learning.
- Parameter Optimizers - Implements algorithms for optimizing model weights through stochastic parameter updates.
- Random Variate Sampling - Generates random samples from a wide variety of probability distributions with support for reproducible seeding.
- Batch-Aware Distributions - Implements batch-aware distribution semantics allowing probability distributions to operate over multiple dimensions simultaneously.
- Constrained Variable Transformations - Implements reversible and composable bijectors to transform simple random variables into complex distributions.
- Monte Carlo Integration - Calculates expectations of functions over probability distributions using Monte Carlo integration tools.
- Joint Distribution Modeling - Constructs collections of interdependent distributions using the chain rule to enable sampling and joint probability computation.
- Bijectors - Provides bijectors for creating complex probability distributions through invertible and differentiable variable transformations.
- Statistical Analysis Libraries - Provides libraries for applying probability models and statistical methods to analyze complex datasets.
- Functional State Management - Ensures reproducibility of random samples using stateless seeds and functional generators across distributed nodes.
- Probabilistic Modeling - Deep learning and probabilistic modelling.
- Probabilistic Programming - Probabilistic programming language utilizing program transformations.
- TensorFlow Frameworks - First-party library offering autoregressive models and composable bijectors.
